A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Involvement of IKAP in peripheral target innervation and in specific JNK and NGF signaling in developing PNS neurons.

A splicing mutation in the ikbkap gene causes Familial Dysautonomia (FD), affecting the IKAP protein expression levels and proper development and function of the peripheral nervous system (PNS). Here we attempted to elucidate the role of IKAP in PNS development in the chick embryo and found that IKAP is required for proper axonal outgrowth, branching, and peripheral target innervation. Moreover, we demonstrate that IKAP colocalizes with activated JNK (pJNK), dynein, and ß-tubulin at the axon terminals of dorsal root ganglia (DRG) neurons, and may be involved in transport of specific target derived signals required for transcription of JNK and NGF responsive genes in the nucleus. These results suggest the novel role of IKAP in neuronal transport and specific signaling mediated transcription, and provide, for the first time, the basis for a molecular mechanism behind the FD phenotype.